Background Socio-spatial health inequalities are increasing in England, as in many other high-income countries. While most research focuses on inequalities between the extreme ends of the deprivation continuum, a less recognised but critical pattern is that the largest gap within the socio-spatial gradient occurs at its lower end - between the two most deprived fifths. Our aim was to understand the drivers of this gap.Methods Using aggregate-level data from official national statistics for a period 2015-2022 and Blinder-Oaxaca decomposition, we provide the first empirical examination of the contributors to the life expectancy gap between the most and second most deprived fifths of local authorities in England and compare these findings with the more conventional comparison between the most and least deprived areas. We assess the relative contributions of material, behavioural, psychosocial, and political economy factors.Results We find that household income accounts for around a third of the life expectancy gap between the two most deprived fifths of local authorities for both men and women (30% and 35%, respectively). It contributes more to inequality at the bottom end of the socio-spatial distribution than between the most and least deprived local authorities. Welfare benefit losses are the next greatest contributor.Conclusion Household income and welfare benefit losses were the strongest contributors to life expectancy inequalities between the two most deprived fifths of local authorities, highlighting the importance of income and wider political economy determinants of health inequalities.
Background Historical redlining, carried out by the Home Owners' Loan Corporation (HOLC), assigned US neighbourhoods grades A ('best') to D ('hazardous'), institutionalising residential segregation and leaving lasting effects on neighbourhood development and health outcomes. There is limited research examining the association between residence in historically redlined areas, present-day neighbourhood socioeconomic status and chronic disease in Baltimore, Maryland.Methods We used 2015-2019 data from the AIDS Linked to the IntraVenous Experience (ALIVE) cohort, an observational study of adults with a history of injection drug use living in or near Baltimore. We included 1335 participants (6568 visits), whose addresses could be assigned an HOLC grade and a 2015 Area Deprivation Index (ADI) percentile. Outcomes included eight chronic health outcomes for which we ran three logistic regression models: HOLC alone, ADI alone and both exposures with an interaction term.Results ALIVE participants were predominantly older (median age: 53.5), black (80%) and male (67%). Relative to grade D, prevalence of hypertension was 14% and 6% higher in grades A and B and prevalence of liver disease was 5-6% higher in grades B and C. Overall, ADI was not associated with any outcome. Within grade D, we saw increases of 3%-5% in prevalence of hypertension, HIV, renal disease, lung disease and multimorbidity per 25-percentile increase in ADI.Conclusions HOLC grade, ADI and chronic conditions had at most modest relationships. Select chronic diseases had lower prevalence in grade D than in areas with higher grades, which may reflect changes in community resources since the 1930s.
This essay traces the two-decade development of multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA). It begins at a course in London in 1999, where I first saw a clear contextual association coexist with an intraclass correlation of only 1%. This paradox exposed two paradigms in epidemiology: one on group averages, the other on the heterogeneity around them. In this Journal, beginning with a 2003 editorial, the heterogeneity paradigm crystallised into a framework of three components: the specific contextual effect, the general contextual effect and discriminatory accuracy. The three must be read together: a large difference between contextual means can coexist with almost no ability to classify individuals, which measures Rose's prevention paradox. MAIHDA is a conceptual framework rather than a statistical technique, agnostic to context definition, statistical method and social theory. It analyses unidimensional contexts, such as geographical areas or healthcare institutions, and multidimensional contexts, such as intersectional strata and other cross-classifications. Intersectionality is just one of the theories MAIHDA can carry. None of the three components requires a random-effects model, and for describing inequalities, simple-means MAIHDA is canonical, avoiding untenable exchangeability assumptions and shrinkage. MAIHDA extends to causal inference: in a weighted pseudo-population it maps the heterogeneity of causal effects and how far that heterogeneity is contextually organised. As an alternative to mean-centric risk-factor epidemiology, MAIHDA harmonises population and precision epidemiology, and makes the mapping of health inequalities a civic function.
While econometrics and statistics drove modern quasi-experimental methods for causal inference in policy evaluation, public health frequently adopts such tools to evaluate health interventions when randomised controlled trials are not feasible. However, public health researchers unfamiliar with the causal inference literature can become lost due to methodological gaps and non-uniform vocabulary between these disciplines. Contending that terminology is the primary source of misunderstanding for a beginner audience, we provide a clear, structured overview of key concepts and terms for public health evaluations, identifying synonyms and misleading words across fields. We also address issues relating to model specification and the interpretation of model parameter estimates. In doing so, we place specific emphasis on the interrupted time series (ITS) design, one of the most widely applied approaches to policy evaluation in public health when using observational data. Finally, we address a common misconception in the application of autoregressive, integrated, moving average (ARIMA) models, the most frequently used regression technique in ITS analysis.
